Bekiroglu, Yasemi

Abstract [en]

Condition monitoring of wooden railway sleepers applications are generally
carried out by visual inspection and if necessary some impact acoustic examination is
carried out intuitively by skilled personnel. In this work, a pattern recognition solution
has been proposed to automate the process for the achievement of robust results. The
study presents a comparison of several pattern recognition techniques together with
various nonstationary feature extraction techniques for classification of impact
acoustic emissions. Pattern classifiers such as multilayer perceptron, learning cector
quantization and gaussian mixture models, are combined with nonstationary feature
extraction techniques such as Short Time Fourier Transform, Continuous Wavelet
Transform, Discrete Wavelet Transform and Wigner-Ville Distribution. Due to the
presence of several different feature extraction and classification technqies, data
fusion has been investigated. Data fusion in the current case has mainly been
investigated on two levels, feature level and classifier level respectively. Fusion at the
feature level demonstrated best results with an overall accuracy of 82% when
compared to the human operator.